A rationale for a course of research into
Nervous NetsJohn A. deVries II -- April 21, 2000

To misquote the song "As Time Goes By", a car is but a car -- in
other words, some technological elements of our society will probably
never change size or shape to any radical degree. However, with the
increasing feasibility of smaller and smaller devices eventually
resulting in an all-pervasive nanotechnology, the way control is to
be achieved must become smaller and simpler as well.

Some of the areas where Nv
nets1have an
essential edge over what has become the de facto use of
microprocessors for control are simplicity, adaptability, and low
power consumption. Simply to get a four-legged robot to walk with any
facility requires a microprocessor consisting of anywhere from 10,000
to one million gates (circuit elements). The typical walker that uses
a Nv
net requires perhaps only 80 gates or so. Furthermore, the
dynamic, analog implicit sensing of motor loads (called
implex2) that occurs using Nv
nets could only be emulated with a great deal of software on a
microprocessor (if at all).

In nature, one finds many oscillators in the form of central
pattern generators; the bicore
is a human-made equivalent. Given matched components the frequency
and duty cycle can be predictably set; substituting a component that
reacts to external stimulus permits variability. Wilf Rigter has
discussed both sorts of bicore
on the BEAM emailing list3 and Wouter
Brok's paper4 has also done so in
some detail.

It is, for example, fairly easy to build a visual core or "head"
using a bicore
and a motor that follows a light or heat source and incidentally
produces a signal representative of the direction. The paradigm of
biological nervous systems then suggests a division of effort -- in
this case, between sensor and motor functions. Most simply, one
bicore
can be used to process sensor information and another bicore
can be used to provide motor control. If one accepts a hierarchy
between sense and motion, the motor control bicore
would act as a slave
to the sensor processing bicore.
Connecting two bicores
so that one is master
and the other is slave
is also called embedding -- embedding one bicore
into another in this manner is one possible starting point for
building a kind of "spinal column"5.
For an inductive model of "robotic evolution", this would represent
the "3D" case.

One can imagine embedding (or surrounding) bicore
within bicore
within bicore
and so forth that would give something that looks like a complicated
bulls-eye. When considered in three dimensional space rather than on
the plane of a piece of paper it could be seen as a perspective view
of a tunnel, the centermost bicore
being furthest away, containing the primary or first sensor. It can
be compared to a simplified neural tube where each bicore
is a ganglion which has motor neurons
that lead out on each "side"; in other words, the basic model that
controls worms and people alike. Implex can be used to alter the
behavior of a single ganglion, providing the kind of control that is
needed locally. Much of this is contained in "Living
Machines"6 although the concept of
the neural tube wasn't covered at that time. Presently, a robot named
BEAMAnt 6.x is the most basic device using this two-bicore
model. It has local "bump" sensors implemented using touch switches
and Nu
(integrating) neurons which alter the action of the motor
control.

The next item, inductively speaking, is the "Snakebot" (also known
as gPIM 2.0), created by Mark Tilden about 1995. Snakebot is a
three-ganglion robot that doesn't have a head. As a result, it isn't
merely a linear tube of three bicores
but instead is a torus. The device is thus not much like a snake but
it does a fair job of behaving like a blind worm. People have
reported the sensation when it is held to be unpleasant. Perhaps
snakes feel (muscularly) at least a little bit more pleasant than
strong worms. gPIM 2.0 is still, however, merely a linear step. The
longest example of this technology to date is the Lampbot 1.0 that is
also known as the "Sidewinder". It consists of a head followed by a
chain 8 segments long, each segment controlling one actuator and
moves much like a lamprey

One can see that only the simplest and most linear of network
topologies (including the single Nv
neuron, loops, unterminated single chains, pairs of neurons
and chains of paired neurons)
have been explored so far. Unfortunately, very little of any other
advanced research that might exist hasn't been published nor even
described in abstracts. However, what information that
does exist seems to predict a continued linear
development of the concept.

The multitude of devices that might maintain the half-century
dream of an automated house7 simply
cannot be controlled using such a linear paradigm, both for the
individual unit and for whatever "community" of units that would be
required. Unless a new plan of analysis and development for nervous
nets (or some other such scalable system) comes into being, we won't
have the tools necessary to support such applications.

Possible research directions might include (but wouldn't be
limited to):

An analysis of Sidewinder

What networks will produce effective artificial ganglia

What can be done to process the signals coming out of a given ganglion

What other network configurations produce interesting or useful results.